Block matching is well known as the most typical method for motion estimation. In order to estimate a global motion amount between plural images (typically two images including one current image and one reference image) in an image sequence, basically a weight average of motion vectors (MV: Motion Vector, also referred to as “local MV”), which are generated for each divided block, over the entire image is taken. In this case, the robustness may be improved by reducing the weight of block having less unreliable local MV (for example, see Japanese Patent Application Laid-Open No. 5-289159 and Japanese Patent Application Laid-Open No. 2006-222933). However, the method for executing block matching is not efficient in general since a large amount of computational resource of block matching to obtain the motion vector MV for each block is required.
As another motion estimation method, there is motion estimation for the entire image. As a motion estimation method for the entire image, LK method (Lucas-Kanade method) is known (see An Iterative Image Registration Technique with an Application to Stereo Vision”, B. D. Lucas, T. Kanade, Intl. Joint Conf. on AI, pp. 674-679, 1981, also referred to as Non-Patent Document 1). When the LK method for the entire image is used, a global motion search process GME with good calculation efficiency can be performed, compared to block matching.
On the other hand, as a motion estimation method to improve robustness, it is known that an image segmentation (also referred to as a screen segmentation) is executed (See Japanese Patent Application Laid-Open No. 2004-015376). In the method executing this image segmentation, input image is divided into plural images and motion estimation is executed for each divided image. By weighting motion vectors MV, which are calculated for each divided image based on reliability of the divided image, the robustness can be improved.